Markov Chains and Limit question : Where have I gone wrong?

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In summary, if you have indexes k and n that are not independent, you should swap k for n-3 in order to get the correct sum. The sum is actually \sum_{n=3}^{\infty}\left(\frac{n}{n+1}\right)^{a(n-3)}-\left(\frac{n}{n+1}\right)^{a(n-2)} which is different from the previous sum of \sum_{n=1}^{\infty}\left(\frac{n}{n+1}\right)^{a(n)} which is correct. However, when n=3, the sum is actually \left(\frac{n}{n+1}\
  • #1
Firepanda
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I am doing question 2 here:

2qtzdx4.png


My solution so far is this:

5eg5dw.jpg


Where have I gone wrong? I'm assuming I'm taking the wrong limit but I'm really stumped here and no idea what else it should be..

I hope my writing is ok, I tried to be as neat as possible!

Thanks
 
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  • #2
Your indexes k and n are not independent, but it looks like you have "independent" limits and terms in the sums, although I believe you have taken the limit with k = n. If I understood correctly, you should have k = n-3. If you plug this in you should get something else.
 
  • #3
I did initially have k = n-3, but I swapped to k because I thought the n's on the LHS and the RHS were different (and independant), and it was confusing me.

Putting k = n-3 does it really change anything? I can't see what it changes..

Thanks
 
  • #4
I'm sorry, it doesn't change a thing. But now I noticed that the sum isn't calculated correctly, you should change k back to n-3 so you can see the flaw. The sum is actually
[itex] \sum_{n=3}^{\infty}\left(\frac{n}{n+1}\right)^{a(n-3)}-\left(\frac{n}{n+1}\right)^{a(n-2)}[/itex] so the cancellations are not correct.
 
Last edited:
  • #5
TheFurryGoat said:
I'm sorry, it doesn't change a thing. But now I noticed that the sum isn't calculated correctly, you should change k back to n-3 so you can see the flaw. The sum is actually
[itex]\sum_{n=3}^\infty{\left(\frac{n}{n+1}\right)^{a(n-3)}-\left(\frac{n}{n+1}\right)^{a(n-2)}[/itex] so the cancellations are not correct.

starting at n=3 I get (1 - Na) : n=4 I get (Na - N2a)

etc

Isn't it the same cancellation?
 
  • #6
Aren't consecutive terms at indexes n, n+1 like so:

[itex]a_n= \left(\frac{n}{n+1}\right)^{a(n-3)}-\left(\frac{n}{n+1}\right)^{a(n-2)}[/itex]
[itex]a_{n+1}= \left(\frac{n+1}{n+2}\right)^{a(n-2)}-\left(\frac{n+1}{n+2}\right)^{a(n-1)}[/itex]?
 
  • #7
TheFurryGoat said:
Aren't consecutive terms at indexes n, n+1 like so:

[itex]a_n= \left(\frac{n}{n+1}\right)^{a(n-3)}-\left(\frac{n}{n+1}\right)^{a(n-2)}[/itex]
[itex]a_{n+1}= \left(\frac{n+1}{n+2}\right)^{a(n-2)}-\left(\frac{n+1}{n+2}\right)^{a(n-1)}[/itex]?

Ah of course, woops!

where do I go from here, I assume I can't cancel any of them, right?

Not sure how to sum that series either..
 
  • #8
Yes, it looks like a very tough series to me. I don't know where to go from there. I don't know anything about Markov chains so I don't know how you calculate the [itex]f_n^i[/itex] 's. Did you calculate them by [itex]T^{(n)}=T(1)\cdots T(n)?[/itex]
cause it looks to me like you should have [itex]f_3^3 = 1/2 \cdot 1 \cdot (1-\frac{3}{3+1})^a[/itex]
[itex]f_3^4 = 1/2 \cdot 1 \cdot (\frac{3}{3+1})^a \cdot (1-\frac{4}{4+1})^a[/itex]
[itex]f_3^5 = 1/2 \cdot 1 \cdot (\frac{3}{3+1})^a \cdot (\frac{4}{4+1})^a \cdot (1-\frac{5}{5+1})^a[/itex]
and so forth. Mayby I'm not interpreting the probabilities correctly, as I said, I'm not familiar with these...
 
  • #9
If the system is in state 5 at time n, the probability that it remains in state 5 at times n+1, n+2,...,n+k is
[tex] \left(\frac{n}{n+1}\right)^a \left(\frac{n+1}{n+2}\right)^a \cdots \left(\frac{n+k-1}{n+k}\right)^a = \left( \frac{n}{n+k}\right)^a, [/tex]
which --> 0 as k --> infinity. You can fix up this argument to show that [itex] \Pr \{ X_{n+k}=5 \; \mbox{ i.o. } |X_n = 5 \} = 0, [/itex], so state 5 is left with probability 1 at a finite time. When that happens, the system returns to state 3, implying that state 3 is recurrent.

RGV
 
  • #10
If it is calculated like I suspect it is, then you will get a satisfying answer.
So first we want to find out what [itex]f_3^n[/itex] is when [itex]n\geq 3:[/itex]

[itex]f_3^n = \displaystyle \frac{1}{2}\cdot1\cdot\left(\prod_{k=3}^{n-1}\left(\frac{k}{k+1}\right)^a\right)\cdot\left(1-\left(\frac{n}{n+1}\right)^a\right) = \frac{1}{2}\cdot\left(\frac{(n-1)!}{2}\cdot\frac{2\cdot3}{n!}\right)^a\cdot\left(1-\left(\frac{n}{n+1}\right)^a\right)[/itex]
[itex] \displaystyle = \frac{1}{2} \cdot \left( \frac{3}{n} \right)^a \cdot \left(1-\left( \frac{n}{n+1} \right)^a \right) = \frac{3^a}{2} \cdot \left(\frac{1}{n^a}- \frac{1}{(n+1)^a}\right)[/itex]
So now the sum of the probabilities is easy to calculate, and I believe that you will finally get the correct answer.
 

1. What is a Markov Chain?

A Markov Chain is a mathematical model that is used to describe the sequence of events in a system where the probability of transitioning from one state to another depends only on the current state and not on the previous states.

2. How do Markov Chains work?

Markov Chains work by representing a system as a series of states and the probabilities of transitioning from one state to another. The transition probabilities are usually represented in a matrix called the transition matrix. The chain then moves from one state to another based on these probabilities.

3. What is a stationary distribution in Markov Chains?

A stationary distribution in Markov Chains is the long-term probability distribution of the states in the chain. It is the distribution that the chain converges to after running for an infinite number of steps.

4. What is the limit question in Markov Chains?

The limit question in Markov Chains asks what happens to the chain as the number of steps approaches infinity. It is used to determine the stationary distribution and the long-term behavior of the system.

5. Where can Markov Chains be applied?

Markov Chains have a wide range of applications in various fields such as economics, finance, physics, biology, and computer science. They are commonly used to model systems with random or unpredictable behavior, such as stock prices, weather patterns, and genetic mutations.

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